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Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

Use this Skill: https://skilld.dev/gh/davila7/claude-code-templates/scientific-schematics

This session only. Nothing lands on disk.

QUICK_REFERENCE.md

≈1.2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Scientific Schematics - Quick Reference

How it works: Describe your diagram → Nano Banana Pro generates it automatically

Setup (One-Time)

# Get API key from https://openrouter.ai/keys
export OPENROUTER_API_KEY='sk-or-v1-your_key_here'

# Add to shell profile for persistence
echo 'export OPENROUTER_API_KEY="sk-or-v1-your_key"' >> ~/.bashrc  # or ~/.zshrc

Basic Usage

# Describe your diagram, Nano Banana Pro creates it
python scripts/generate_schematic.py "your diagram description" -o output.png

# That's it! Automatic:
# - Iterative refinement (3 rounds)
# - Quality review and improvement
# - Publication-ready output

Common Examples

CONSORT Flowchart

python scripts/generate_schematic.py \
  "CONSORT flow: screened n=500, excluded n=150, randomized n=350" \
  -o consort.png

Neural Network

python scripts/generate_schematic.py \
  "Transformer architecture with encoder and decoder stacks" \
  -o transformer.png

Biological Pathway

python scripts/generate_schematic.py \
  "MAPK pathway: EGFR → RAS → RAF → MEK → ERK" \
  -o mapk.png

Circuit Diagram

python scripts/generate_schematic.py \
  "Op-amp circuit with 1kΩ resistor and 10µF capacitor" \
  -o circuit.png

Command Options

Option Description Example
-o, --output Output file path -o figures/diagram.png
--iterations N Number of refinements (1-2) --iterations 2
-v, --verbose Show detailed output -v
--api-key KEY Provide API key --api-key sk-or-v1-...

Prompt Tips

✓ Good Prompts (Specific)

  • "CONSORT flowchart with screening (n=500), exclusion (n=150), randomization (n=350)"
  • "Transformer architecture: encoder on left with 6 layers, decoder on right, cross-attention connections"
  • "MAPK signaling: receptor → RAS → RAF → MEK → ERK → nucleus, label each phosphorylation"

✗ Avoid (Too Vague)

  • "Make a flowchart"
  • "Neural network"
  • "Pathway diagram"

Output Files

For input diagram.png, you get:

  • diagram_v1.png - First iteration
  • diagram_v2.png - Second iteration
  • diagram_v3.png - Final iteration
  • diagram.png - Copy of final
  • diagram_review_log.json - Quality scores and critiques

Review Log

{
  "iterations": [
    {
      "iteration": 1,
      "score": 7.0,
      "critique": "Good start. Font too small..."
    },
    {
      "iteration": 2,
      "score": 8.5,
      "critique": "Much improved. Minor spacing issues..."
    },
    {
      "iteration": 3,
      "score": 9.5,
      "critique": "Excellent. Publication ready."
    }
  ],
  "final_score": 9.5
}

Python API

from scripts.generate_schematic_ai import ScientificSchematicGenerator

# Initialize
gen = ScientificSchematicGenerator(api_key="your_key")

# Generate
results = gen.generate_iterative(
    user_prompt="diagram description",
    output_path="output.png",
    iterations=2
)

# Check quality
print(f"Score: {results['final_score']}/10")

Troubleshooting

API Key Not Found

# Check if set
echo $OPENROUTER_API_KEY

# Set it
export OPENROUTER_API_KEY='your_key'

Import Error

# Install requests
pip install requests

Low Quality Score

  • Make prompt more specific
  • Include layout details (left-to-right, top-to-bottom)
  • Specify label requirements
  • Increase iterations: --iterations 2

Testing

# Verify installation
python test_ai_generation.py

# Should show: "6/6 tests passed"

Cost

Typical cost per diagram (max 2 iterations):

  • Simple (1 iteration): $0.05-0.15
  • Complex (2 iterations): $0.10-0.30

How Nano Banana Pro Works

Simply describe your diagram in natural language:

  • ✓ No coding required
  • ✓ No templates needed
  • ✓ No manual drawing
  • ✓ Automatic quality review
  • ✓ Publication-ready output
  • ✓ Works for any diagram type

Just describe what you want, and it's generated automatically.

Getting Help

# Show help
python scripts/generate_schematic.py --help

# Verbose mode for debugging
python scripts/generate_schematic.py "diagram" -o out.png -v

Quick Start Checklist

  • Set OPENROUTER_API_KEY environment variable
  • Run python test_ai_generation.py (should pass 6/6)
  • Try: python scripts/generate_schematic.py "test diagram" -o test.png
  • Review output files (test_v1.png, v2, v3, review_log.json)
  • Read SKILL.md for detailed documentation
  • Check README.md for examples

Resources

  • Full documentation: SKILL.md
  • Detailed guide: README.md
  • Implementation details: IMPLEMENTATION_SUMMARY.md
  • Example script: example_usage.sh
  • Get API key: https://openrouter.ai/keys

Source: SKILL.md on GitHub

1 warning17d4 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The scientific-schematics skill allows users to generate publication-quality diagrams via the OpenRouter API. It follows security best practices for credential management by utilizing environment variables and .env files. The skill is functionally safe but exhibits a surface for indirect prompt injection because user-provided descriptions are interpolated into model prompts without sanitization or boundary markers.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    8/8 files flagged

Signed by skilld at 2fe0cfa. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

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